Summary

(No information yet)

License

unknown (from Weka repository)

Dependencies

Tags

arff slurped Weka

Attribute Types

Integer,Floating Point

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# Instances: 5473 / # Attributes: 11
HDF5 (335.6 KB) XML CSV ARFF LibSVM Matlab Octave

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Original Data Format

arff

Name

page-blocks

Version mldata

0

Comment

1. Title of Database: Blocks Classification
2. Sources: (a) Donato Malerba Dipartimento di Informatica University of Bari via Orabona 4 70126 Bari - Italy phone: +39 - 80 - 5443269 fax: +39 - 80 - 5443196 malerbad@vm.csata.it (b) Donor: Donato Malerba (c) Date: July 1995
3. Past Usage: This data set have been used to try different simplification methods for decision trees. A summary of the results can be found in:

Malerba, D., Esposito, F., and Semeraro, G. "A Further Comparison of Simplification Methods for Decision-Tree Induction." In D. Fisher and H. Lenz (Eds.), "Learning from Data: Artificial Intelligence and Statistics V", Lecture Notes in Statistics, Springer Verlag, Berlin, 1995.

The problem consists in classifying all the blocks of the page layout of a document that has been detected by a segmentation process. This is an essential step in document analysis in order to separate text from graphic areas. Indeed, the five classes are: text (1), horizontal line (2), picture (3), vertical line (4) and graphic (5). For a detailed presentation of the problem see:

Esposito F., Malerba D., & Semeraro G.

Multistrategy Learning for Document Recognition Applied Artificial Intelligence, 8, pp. 33-84, 1994

All instances have been personally checked so that low noise is present in the data.

1. Relevant Information Paragraph:

The 5473 examples comes from 54 distinct documents. Each observation concerns one block. All attributes are numeric. Data are in a format readable by C4.5.

1. Number of Instances: 5473.

2. Number of Attributes

height: integer. | Height of the block. lenght: integer. | Length of the block. area: integer. | Area of the block (height * lenght); eccen: continuous. | Eccentricity of the block (lenght / height); p_black: continuous. | Percentage of black pixels within the block (blackpix / area); p_and: continuous. | Percentage of black pixels after the application of the Run Length Smoothing Algorithm (RLSA) (blackand / area); mean_tr: continuous. | Mean number of white-black transitions (blackpix / wb_trans); blackpix: integer. | Total number of black pixels in the original bitmap of the block. blackand: integer. | Total number of black pixels in the bitmap of the block after the RLSA. wb_trans: integer. | Number of white-black transitions in the original bitmap of the block.

1. Missing Attribute Values: No missing value.

2. Class Distribution:

                                      Valid    Cum
   

   Class Frequency Percent Percent Percent

text 4913 89.8 89.8 89.8 horiz. line 329 6.0 6.0 95.8 graphic 28 .5 .5 96.3 vert. line 88 1.6 1.6 97.9 picture 115 2.1 2.1 100.0 ------- ------- ------- TOTAL 5473 100.0 100.0

Summary Statistics:

Variable Mean Std Dev Minimum Maximum Correlation

HEIGHT 10.47 18.96 1 804 .3510 LENGTH 89.57 114.72 1 553 -.0045 AREA 1198.41 4849.38 7 143993 .2343 ECCEN 13.75 30.70 .007 537.00 .0992 P_BLACK .37 .18 .052 1.00 .2130 P_AND .79 .17 .062 1.00 -.1771 MEAN_TR 6.22 69.08 1.00 4955.00 .0723 BLACKPIX 365.93 1270.33 7 33017 .1656 BLACKAND 741.11 1881.50 7 46133 .1565 WB_TRANS 106.66 167.31 1 3212 .0337

Information about the dataset CLASSTYPE: nominal CLASSINDEX: last

Names

height,lenght,area,eccen,p_black,p_and,mean_tr,blackpix,blackand,wb_trans,

Types

1. numeric
2. numeric
3. numeric
4. numeric
5. numeric
6. numeric
7. numeric
8. numeric
9. numeric
10. numeric

Data (first 10 data points)

height	lenght	area	eccen	p_bl...	p_and	mean...	blac...	blac...	wb_t...	...
5	7	35	1.4	0.4	0.657	2.33	14	23	6	...
6	7	42	1.167	0.429	0.881	3.6	18	37	5	...
6	18	108	3.0	0.287	0.741	4.43	31	80	7	...
5	7	35	1.4	0.371	0.743	4.33	13	26	3	...
6	3	18	0.5	0.5	0.944	2.25	9	17	4	...
5	8	40	1.6	0.55	1.0	2.44	22	40	9	...
6	4	24	0.667	0.417	0.708	2.5	10	17	4	...
5	6	30	1.2	0.333	0.333	10.0	10	10	1	...
5	5	25	1.0	0.4	0.52	10.0	10	13	1	...
5	7	35	1.4	0.486	0.914	8.5	17	32	2	...
...	...	...	...	...	...	...	...	...	...	...

Description

A gzip'ed tar containing UCI and UCI KDD datasets (uci-20070111.tar.gz, 17,952,832 Bytes)

URLs

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Publications

Data Source

http://www.ics.uci.edu/~mlearn/MLRepository.html http://kdd.ics.uci.edu/

Measurement Details

Usage Scenario

revision 1

by mldata on 2011-09-14 16:04

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